Machine Learning Techniques and Training | 4th | week 2 |.
Machine Learning is a broad field and we can split it up into three different categories, Supervised Learning, Unsupervised Learning, and Reinforcement Learning. There are many different tasks we can solve with these. Supervised Learning refers to when we have class labels in the dataset and we use these to build the classification model. What this means is when we receive data, it has labels that say what the data represents. In a previous example, we had a table with labels such as age or sex. With Unsupervised Learning, we don't have class labels and we must discover class labels from unstructured data. This could involve things such as deep learning looking at pictures to train models. Things like this are typically done with something called clustering. Reinforcement Learning is a different subset, and what this does is it uses a reward function to penalize bad actions or reward good actions. Breaking down Supervised Learning, we can split it up into three categories, Regression, Classification and Neural Networks. Regression models are built by looking at the relationships between features x and the result y where y is a continuous variable. Essentially, Regression estimates continuous values. Neural Networks refer to structures that imitate the structure of the human brain. Classification on the other hand, focuses on discrete values it identifies. We can assign discrete class labels y based on many input features x. In a previous example, given a set of features x, like beats per minute, body mass index, age and sex, the algorithm classifies the output y as two categories, True or False, predicting whether the heart will fail or not. In other Classification models, we can classify results into more than two categories.